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Statistics for Computational Economics

Thomas J. Sargent and John Stachurski

Index

A | C | L | M

A

A Problem that Stumped Milton Friedman

C

Central Limit Theorem, [1]
- Intuition
- Multivariate Case

CLT

L

Law of Large Numbers, [1]

LLN

M

Models
- Sequential analysis

Creative Commons License – This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International.

Contents

Elementary Statistics

1. Linear Regression in Python
2. Maximum Likelihood Estimation
3. Elementary Probability with Matrices
4. LLN and CLT
5. Multivariate Normal Distribution

Information & Bayesian Statistics

6. Two Meanings of Probability
7. Non-Conjugate Priors
8. Posterior Distributions for AR(1) Parameters
9. Forecasting an AR(1) Process
10. Likelihood Ratio Processes
11. Computing Mean of a Likelihood Ratio Process
12. A Problem that Stumped Milton Friedman
13. Exchangeability and Bayesian Updating
14. Likelihood Ratio Processes and Bayesian Learning
15. Incorrect Models
16. Bayesian versus Frequentist Decision Rules

Applications of Statistics

17. Multivariate Hypergeometric Distribution
18. Fault Tree Uncertainties
19. Introduction to Artificial Neural Networks
20. Randomized Response Surveys
21. Expected Utilities of Random Responses

Other

22. Troubleshooting
23. References
24. Execution Statistics

QuantEcon

Lecture (PDF)
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